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dc.contributor.authorAbbaspour_Gilandeh, Yousef
dc.contributor.authorSabzi, Sajad
dc.contributor.authorHernández Hernández, Mario
dc.contributor.authorHernández-Hernández, José Luis
dc.contributor.authorAzadshahraki, Farzad
dc.creatorAbbaspour_Gilandeh, Yousef;#0000-0002-9999-7845
dc.creatorSabzi, Sajad;#0000-0003-2439-5329
dc.creatorHernández Hernández, Mario;#0000-0001-8330-4779
dc.creatorHernández-Hernández, José Luis;#0000-0003-0231-2019
dc.creatorAzadshahraki, Farzad;#0000-0002-7261-7849
dc.date.accessioned2023-03-21T20:03:39Z
dc.date.available2023-03-21T20:03:39Z
dc.date.issued2019-11
dc.identifier.issndoi:10.3390/agronomy9110735
dc.identifier.urihttp://ri.uagro.mx/handle/uagro/3499
dc.description.abstractNondestructive estimation of the various physicochemical features of food such as fruits and vegetables will create a dramatic development in the food industry. The reason for this development is that the estimation is non-destructive, online, and most importantly fast. Regarding the advantages, various researchers have focused on how to undertake non-destructive estimation of the physicochemical features of various nutrients. Three main goals were pursued in this article. These are: 1. Nondestructive estimation of the chlorophyll b content of red delicious apple using color features and hybrid artificial neural network-cultural algorithm (ANN-CA), 2. Nondestructive estimation of chlorophyll b content of red delicious apple using spectral data (around a range of 680 nm) and hybrid Artificial Neural Network-biogeography-based algorithm (ANN-BBO), 3. Nondestructive estimation of the chlorophyll b content of red delicious apple using different groups of selective spectra by the hybrid artificial neural network-differential evolution algorithm (ANN-DA). In each of these methods, 1000 replications were performed to evaluate the reliability of various hybrids of the artificial neural network. Finally, the results indicated that the average determination coefficient in 1000 replications for the hybrid artificial neural network, the cultural algorithm, and the hybrid artificial neural network, the biogeography-based optimization algorithm, was 0.882 and 0.932, respectively. Also, the results showed that the highest value of the coefficient of determination among the different groups of effective features is related to the group of features with 10 spectra. The coefficient of determination in this case was 0.93.
dc.formatpdf
dc.language.isoeng
dc.publisherAgronomy
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectnon-destructive
dc.subjectcolor features
dc.subjectspectral data
dc.subjecteffective spectra
dc.subjectvisible light/near-infrared
dc.subjecthybrid artificial neural network
dc.subject.classificationINGENIERÍA Y TECNOLOGÍA::CIENCIAS TECNOLÓGICAS::TECNOLOGÍA BIOQUÍMICA::HIGIENE DE LOS ALIMENTOS
dc.titleNondestructive Estimation of the Chlorophyll b of Apple Fruit by Color and Spectral Features Using Different Methods of Hybrid Artificial Neural Network.
dc.typeArtículo
dc.type.conacytarticle
dc.rights.accesopenAccess
dc.audiencegeneralPublic
dc.identificator7||33||3302||330915
dc.format.digitalOriginBorn digital
dc.thesis.degreelevelDoctorado
dc.thesis.degreenameDoctorado en Innovación y Cultura Digital
dc.thesis.degreegrantorUniversidad Autónoma de Guerrero
dc.thesis.degreedepartmentFacultad de Ingeniería
dc.thesis.degreedisciplineCiencias Sociales
dc.identifier.cvuagro11228


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Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0